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DEVLOG.md — Audit AI

Project: AI Spend Audit Author: Vikas Singh - vikasngh0897

📅 Day 1 — 08 May 2026

Focus: Infrastructure, CI/CD, and Pricing Research Hours Worked: 4h

✅ What I Accomplished

  • Project Setup: Initialized a MERN project repository with Express v5 and TypeScript v6.
  • CI/CD Pipeline: Set up GitHub Actions to run linting and build checks on every push.
  • AI Pricing Data: Created PRICING_DATA.md. Researched and documented current pricing tiers for Cursor, Claude, and ChatGPT.

💡 What I Learned

  • Enterprise CI/CD Integration: Gained hands-on experience in configuring industry-standard CI/CD pipelines and version control workflows to ensure seamless, automated deployments.
  • Professional Project Architecture: Mastered the structural requirements of production-grade monorepos, focusing on scalability, strict type-safety, and maintainable folder hierarchies.

🚧 Blockers

  • N/A

🎯 Plan for Tomorrow

  • Data Architecture & Persistence: Design and implement the MongoDB schemas to handle session persistence and the generation of unique, shareable audit URLs.

  • Controller Logic & API Design: Develop the core backend controllers to execute the "Audit Math" and manage the state between the visitor input and the results dashboard.

  • AI Intelligence Integration: Seamlessly integrate the Anthropic API to parse audit data and generate high-value, personalized executive summaries for users.

📅 Day 2 — 09 May 2026

Focus: Data Modeling, Business Logic, and Middleware Hours Worked: 5h

✅ What I Accomplished

  • Data Architecture: Designed and implemented Mongoose schemas (Models) for AI spend sessions, ensuring persistence for shareable audit URLs.
  • Audit Engine Logic: Developed core backend controllers to handle the "Audit Math," processing user inputs to calculate cost-efficiency and potential savings.
  • Request Validation: Engineered custom Express middleware for robust request validation and error handling (Commit d3862f4).

💡 What I Learned

  • Decoupled Architecture: Deepened expertise in separating business logic from route definitions, maintaining "skinny" controllers and "fat" service layers for better testability.
  • Schema Optimization: Explored advanced Mongoose modeling techniques to balance flexible JSON data (for varying API tiers) with strict TypeScript interfaces.

🚧 Blockers

  • Tier Polymorphism: Identifying the most efficient way to type diverse usage limits (Claude vs. Cursor vs. OpenAI) within TypeScript without creating a bloated or overly complex schema.

🎯 Plan for Tomorrow

  • External Integrations: Build utility functions for the Anthropic API to generate AI-driven executive summaries.
  • Communication Layer: Implement email confirmation services and automated report generation logic.

📅 Day 3 — 10 May 2026

Focus: Utility Modules, AI Integration, and System Resilience Hours Worked: 4h

✅ What I Accomplished

  • AI Intelligence Integration: Successfully integrated the Anthropic API to parse audit data and generate high-value, personalized executive summaries (Commit 1c6b84c).
  • Communication & Security: Implemented transactional email services for report delivery and integrated CAPTCHA to protect the engine from automated abuse.
  • Error Handling Standardization: Developed a centralized response and error-handling utility to ensure consistent API behavior across all endpoints.

💡 What I Learned

  • Resilient System Design: Mastered the implementation of fallback mechanisms for third-party LLM dependencies, ensuring the core audit value is delivered even during external API timeouts.
  • Security vs. UX Balancing: Gained insight into fine-tuning CAPTCHA verification flows to prevent legitimate payload blockage while maintaining high security standards.

🚧 Blockers

  • Verification Friction: Initial CAPTCHA configurations blocked legitimate local testing submissions, requiring a refactor of the verification middleware to distinguish between environments.

🎯 Plan for Tomorrow

  • API Connectivity: Connect the established controllers and utility functions to active Express REST routes.
  • Frontend Integration: Begin bridging the backend logic with the React client to visualize the audit results.

📅 Day 4 — 11 May 2026

Focus: API Orchestration, Route Mapping, and Endpoint Testing Hours Worked: 3h

✅ What I Accomplished

  • API Route Architecture: Successfully wired the Express backend routes, mapping frontend-facing endpoints to their respective audit controllers (Commit cecc8df).
  • Middleware Orchestration: Seamlessly integrated validation and CAPTCHA protection layers into the request lifecycle to ensure secure and sanitized data entry.
  • End-to-End Testing: Conducted comprehensive endpoint validation using Postman to verify "Audit Math" accuracy and error-handling resilience.

💡 What I Learned

  • Modular Router Design: Refined the practice of decoupling routing logic from the main application entry point (app.ts). This modular approach ensures the codebase remains maintainable and scalable as the API surface grows.
  • Protocol Reliability: Validated that the infrastructure built on Days 2 and 3 provides a robust foundation, allowing for rapid endpoint deployment with minimal friction.

🚧 Blockers

  • N/A: The preparatory work on the controller logic and utility modules in previous days eliminated potential bottlenecks, resulting in a smooth integration phase.

🎯 Plan for Tomorrow

  • Frontend Development: Transition to the client-side build, focusing on the landing page and the dynamic spend input form.
  • UI/UX Implementation: Develop the interactive results dashboard and data visualization components using React and Tailwind CSS.

📅 Day 5 — 12 May 2026

Focus: Frontend Architecture, State Management, and UX Design Hours Worked: 6h

✅ What I Accomplished

  • Frontend Core Build: Developed the React/Next.js application, including the high-impact hero section and landing page (Commits ff5f0b9, 25b700b).
  • Dynamic Audit Form: Engineered a multi-step input form for capturing granular user spend data, ensuring a frictionless user journey.
  • State Persistence: Implemented robust state management logic to persist form data across page reloads, preventing data loss during the audit process.

💡 What I Learned

  • Advanced Form Architecture: Mastered the management of complex, nested form states efficiently while maintaining high performance.
  • Performance-First Design: Learned to structure UI components to achieve top-tier Lighthouse scores, focusing on accessibility (a11y) and Cumulative Layout Shift (CLS) optimization.

🚧 Blockers

  • Mobile Data Density: Displaying comprehensive savings data on small screens proved challenging. I am currently experimenting with collapsible data cards to prevent UI clutter on mobile devices.

🎯 Plan for Tomorrow

  • Product Strategy: Shift focus to the entrepreneurial documentation, including GTM strategies and economic modeling.
  • Deployment Prep: Finalize the deployment pipeline for the production launch.

📅 Day 6 — 13 May 2026

Focus: Product Strategy, Unit Economics, and Market Validation Hours Worked: 4h

✅ What I Accomplished

  • Strategy Documentation: Drafted the GTM.md (Go-To-Market) and ECONOMICS.md files, detailing the roadmap for user acquisition and revenue scaling.
  • User Research Synthesis: Conducted and synthesized notes from three real-world user interviews into USER_INTERVIEWS.md to inform future feature prioritization.
  • Economic Modeling: Calculated unit economics and projected the Customer Acquisition Cost (CAC) specifically for AI lead-generation workflows.

💡 What I Learned

  • Product Management Mindset: Transitioned from "building features" to "solving business problems," learning to translate technical efficiency into tangible ROI for stakeholders.
  • Market Benchmarking: Gained experience in using industry benchmarks to project conversion rates and business outcomes when historical data is unavailable.
  • Final Polish: Conduct a comprehensive codebase review and UI/UX audit.
  • Production Launch: Deploy the finalized application and documentation.

🚧 Blockers

  • Conversion Projection: Estimating the specific conversion rate from a completed audit to a booked consultation with Credex remains speculative. I have addressed this by implementing a "Conservative vs. Aggressive" projection model.